I'm a postdoctoral fellow at Chapman University's Brain Institute. My current research is on pushing the boundaries of our current understanding of how consciousness comes to be by triangulating the neurocorrelates of consciousness using machine learning. There are various ways in which objects enter our visual field but we do not see them. My objective is to leverage machine learning to decode from brain waves (EEG) whether participants were aware or not of particular stimuli, in three different no-report tasks across three different laboratories. Previously, I was a visiting professor at Webster University, where I lectured various courses for 2 years, and mentored student research.My PhD was about the short and long-term effects of nicotine and bupropion on neurocognition. I used source localized EEG frequency to identify differences between treatments and placebo at various points in time after tobacco abstinence, and I found that tobacco and BUP have significant differences in how they act on different neural network-related regions, particularly at parietal and cingulate cortices at theta, alpha, and beta frequency bands.